Add 5 skills from LFM + 12 skills total
New skills: - blacknode-graph-workflow - multi-agent-workflow-execution - langgraph-agent-workflow - langgraph-multi-agent-router - three-tier-evaluation-pipeline Config: LLM pipeline uses LFM on llama.cpp (8080)
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Epictetus
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---
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name: langgraph-agent-workflow
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version: 1.0.0
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description: Orchestrate multi-step AI agents using LangGraph with SerperDevTool for
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RAG, code execution, and citation generation
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inputs:
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- LangGraph chain configuration files defining agent workflows
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- SerperDevTool integration for LLM tool access
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- React agent creation scripts via create_react_agent
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- Knowledge graph retrieval and citation generation pipelines
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steps:
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- 'Step 1: Define LangGraph chain architecture with SerperDevTool integration - Create
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a LangGraph chain that combines retrieval, reasoning, and response generation using
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SerperDevTool for tool access'
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- 'Step 2: Implement React agent wrapper - Use create_react_agent to build a frontend
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agent that can interact with the LangGraph chain'
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- 'Step 3: Configure RAG pipeline - Set up vector search (Qdrant/OpenSearch) and knowledge
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graph retrieval (Neo4j/ArangoDB) with citation generation'
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- 'Step 4: Add code execution sandbox - Integrate artifact generation capabilities
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for code-related tasks'
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- "Step 5: Orchestrate multi-step research workflow - Chain search \u2192 deep research\
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\ \u2192 agent response in a single LangGraph workflow"
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outputs:
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- Reusable LangGraph chain definition (pyfile) with configurable steps
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- React agent frontend component that can be deployed independently
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- RAG pipeline that generates block citations and grounded answers
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- Code execution sandbox for artifact generation
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- Documentation for parameterizing workflows for different tasks
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tags: []
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metadata:
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source_repo: https://github.com/pipeshub-ai/pipeshub-ai.git
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extracted_at: ''
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confidence: 0.95
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---
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# langgraph-agent-workflow
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Orchestrate multi-step AI agents using LangGraph with SerperDevTool for RAG, code execution, and citation generation
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## Setup
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**Dependencies:**
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```text
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pip install langgraph>=0.7.0 serper-dev-tool>=0.1.0 qdrant-client or opensearch-dsl neo4j-driver or arango-database-driver react, next.js
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```
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**Setup steps:**
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1. Install LangGraph and SerperDevTool dependencies
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1. Configure vector store (Qdrant/OpenSearch) and knowledge graph (Neo4j/ArangoDB)
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1. Define chain topology with retrieval, reasoning, and response steps
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1. Build React agent frontend using create_react_agent
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1. Test multi-step agent workflows end-to-end
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## Key Files
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- `pipeshub-ai/workflows/agent_chain.py - Main LangGraph chain definition`
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- `pipeshub-ai/workflows/agent_react.py - React agent wrapper`
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- `pipeshub-ai/workflows/rag_pipeline.py - RAG with citation generation`
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- `pipeshub-ai/workflows/code_sandbox.py - Code execution sandbox`
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## Steps
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1. Step 1: Define LangGraph chain architecture with SerperDevTool integration - Create a LangGraph chain that combines retrieval, reasoning, and response generation using SerperDevTool for tool access
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2. Step 2: Implement React agent wrapper - Use create_react_agent to build a frontend agent that can interact with the LangGraph chain
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3. Step 3: Configure RAG pipeline - Set up vector search (Qdrant/OpenSearch) and knowledge graph retrieval (Neo4j/ArangoDB) with citation generation
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4. Step 4: Add code execution sandbox - Integrate artifact generation capabilities for code-related tasks
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5. Step 5: Orchestrate multi-step research workflow - Chain search → deep research → agent response in a single LangGraph workflow
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## Implementation Details
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```python
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chain = LangGraph()
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```
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```python
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chain.add_step(SerperDevToolAgent())
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```
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```python
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agent = create_react_agent(chain, SerperDevToolAgent())
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```
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```python
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workflow = chain.start()
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```
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## Inputs
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- LangGraph chain configuration files defining agent workflows
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- SerperDevTool integration for LLM tool access
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- React agent creation scripts via create_react_agent
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- Knowledge graph retrieval and citation generation pipelines
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## Outputs
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- Reusable LangGraph chain definition (pyfile) with configurable steps
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- React agent frontend component that can be deployed independently
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- RAG pipeline that generates block citations and grounded answers
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- Code execution sandbox for artifact generation
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- Documentation for parameterizing workflows for different tasks
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## Failure Modes
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- GraphDB connection failures if Neo4j/ArangoDB is not properly configured
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- Vector store unavailability (Qdrant/OpenSearch) causing RAG pipeline to fail
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- LLM tool access errors if SerperDevTool is not properly initialized
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- Agent timeout if complex multi-step reasoning exceeds time limits
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- Sandbox execution failures if code has security vulnerabilities or infinite loops
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## Source
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Extracted from: [https://github.com/pipeshub-ai/pipeshub-ai.git](https://github.com/pipeshub-ai/pipeshub-ai.git)
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Confidence: 0.95
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